{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/12"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Converts TensorFlow, Keras, TensorFlow.js, and TFLite models to ONNX format via command line or Python API, enabling model portability across different inference runtimes.","skillfed_tags":["model-conversion","onnx","tensorflow"],"use_cases":["Deploy TensorFlow models on inference engines that don't natively support TensorFlow format","Convert Keras models to ONNX for cross-platform inference on mobile, edge, or cloud deployments","Migrate TFLite models to ONNX when targeting different hardware accelerators or inference frameworks","Enable model interoperability in ML pipelines that mix models from different training frameworks","Export trained models for production serving on systems where TensorFlow runtime is unavailable"],"what_it_does":"tf2onnx is a model converter that takes trained TensorFlow models in various formats (saved_model, checkpoint, graphdef, tflite, tensorflow.js) and translates them into ONNX format. This enables models trained in TensorFlow to run on any ONNX-compatible runtime, improving portability and deployment flexibility. The package works via a command-line interface or Python API, with support for opset versions 14-18 (default 15). It requires TensorFlow 2.13 or later and Python 3.10-3.12.\n\nThe converter handles the fundamental challenge that TensorFlow has more operations than ONNX, so not all models convert cleanly. The project documents supported operations and provides troubleshooting guidance. TensorFlow.js support is experimental. The package is actively maintained but currently seeking a new maintainer, which is worth noting when evaluating long-term reliability.","worth_installing":"Yes, if you need to convert TensorFlow models to ONNX. The package is production-stable, actively maintained, has no known security issues, and installs with low friction. The main caveat is that not all TensorFlow operations map cleanly to ONNX, so conversion success depends on your specific model architecture. The active search for a new maintainer is a yellow flag for long-term support, but current maintenance is solid."},"id":"tf2onnx","links":{"html":"https://skillfed.io/packages/tf2onnx","md":"https://skillfed.io/packages/tf2onnx.md","pypi":"https://pypi.org/project/tf2onnx/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-04","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"tf2onnx","python_support":"supports_current","summary":"Tensorflow to ONNX converter"},"popularity":{"monthly_downloads":395329,"position":6981,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.17.0"}